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This item is available under a Creative Commons License for non-commercial use only

Publication Details

2017 14th International Conference on the European Energy Market (EEM), 6-9 June 2017, Dresden, Germany

Abstract

An increasing number of utilities participating in the energy market require short term (i.e. up to 48 hours) power forecasts for renewable generation in order to optimize technical and financial performances. As a result, a large number of forecast providers now operate in the marketplace, each using different methods and offering a wide range of services. This paper assesses five different day-ahead wind power forecasts generated by various service providers currently operating in the market, and compares their performance against the state-of-the-art of short-term wind power forecasting. The work focuses on how power curve estimations can introduce systematic errors that affect overall forecast performance. The results of the study highlight the importance of: accurately modelling the wind speed-to-power output relationships at higher wind speeds; taking account of power curve trends when training models; and the need to incorporate long-term (months to years) power curve variability into the forecast updating process.

Recommended Citation

G. Goretti, A. Duffy, and T. T. Lie, (2017) “The impact of power curve estimation on commercial wind power forecasts - An empirical analysis,” International Conference on the European Energy Market, EEM, 2017.